replaced pipe_abc with pipe_rc related to the same
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2 changed files with 2 additions and 3 deletions
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@ -19,7 +19,7 @@ def fsgs(input_df
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, var_type = ['numerical'
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, var_type = ['numerical'
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, 'categorical'
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, 'categorical'
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, 'mixed']
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, 'mixed']
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, fs_estimator = [LogisticRegression(**rs)]
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#, fs_estimator = [LogisticRegression(**rs)]
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, fs = RFECV(DecisionTreeClassifier(**rs)
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, fs = RFECV(DecisionTreeClassifier(**rs)
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, cv = StratifiedKFold(n_splits = 10
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, cv = StratifiedKFold(n_splits = 10
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, shuffle = True,**rs)
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, shuffle = True,**rs)
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@ -145,7 +145,6 @@ param_grid_gbc = [
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{
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{
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# 'clf': [GradientBoostingClassifier(**rs)],
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# 'clf': [GradientBoostingClassifier(**rs)],
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'clf__n_estimators' : [10, 100, 200, 500, 1000]
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'clf__n_estimators' : [10, 100, 200, 500, 1000]
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, 'clf__n_estimators' : [10, 100, 1000]
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, 'clf__learning_rate': [0.001, 0.01, 0.1]
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, 'clf__learning_rate': [0.001, 0.01, 0.1]
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, 'clf__subsample' : [0.5, 0.7, 1.0]
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, 'clf__subsample' : [0.5, 0.7, 1.0]
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, 'clf__max_depth' : [3, 7, 9]
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, 'clf__max_depth' : [3, 7, 9]
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@ -354,7 +353,7 @@ param_grid_qda = [
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estimator = RidgeClassifier(**rs)
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estimator = RidgeClassifier(**rs)
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# Define pipleline with steps
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# Define pipleline with steps
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pipe_abc = Pipeline([
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pipe_rc = Pipeline([
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('pre', MinMaxScaler())
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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